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ImpAcX_OnHW: Improving accuracy and explainability of handwriting recognition models

From National Research Council Canada

LinkImpAcX_onHW (GitHub)
DOIResolve DOI: https://doi.org/10.4224/40002946
AuthorSearch for: 1; Search for: 1; Search for: 2; Search for:
Affiliation
  1. University of Waterloo
  2. National Research Council of Canada. Digital Technologies
FormatSoftware, multimedia, Software
Subjectmachine learning; deep learning; handwriting recognition
Abstract
This project contains codes and information to help reproduce, verify and improve results as reported in our paper "Improving Accuracy and Explainability of Handwriting Recognition Models" available at https://arxiv.org/abs/2209.09102
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PublisherNational Research Council of Canada
Copyright statement
  • © National Research Council of Canada
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Related publication
  • Azimi, Hilda, Steven Chang, Jonathan Gold, and Koray Karabina. ‘Improving Accuracy and Explainability of Online Handwriting Recognition’. arXiv, 2022. https://doi.org/10.48550/ARXIV.2209.09102.
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Record identifier5cc37af9-6634-4477-9c65-f595c8bc5c59
Record created2022-10-03
Record modified2022-10-03
Date modified: